Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Constraint Satisfaction and Optimization
Abstract
This paper explores a novel algorithm for non-linear constraint satisfaction, focusing on adaptive variable weight optimization. Traditional approaches often struggle with variables that change over time, leading to suboptimal solutions. We introduce a dynamic variable weighting mechanism that automatically adjusts weights based on observed changes, enabling the algorithm to effectively handle evolving constraints. The core mechanism involves a reinforcement learning-inspired approach to dynamically adjust weights, optimizing the objective function while maintaining constraints. This approach significantly improves the performance of constraint-based optimization problems, particularly when dealing with dynamic and complex environments. The results demonstrate the effectiveness of our algorithm in a range of scenarios, showcasing its ability to adapt to changing conditions and achieve high-quality solutions.
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